Lecture Geometric Deep Learning (MA-INF 4333, 6CP)

Geometric deep learning is a subfield of machine learning that focuses on processing and learning from data with geometric structures, such as graphs, meshes, and point clouds. Due to the often high dimensional and complex data and smaller set of training examples in this field, incorporating prior knowledge about the intrinsic structure and symmetries is essential when designing neural networks. When done right, this enables them to excel in tasks like molecular property prediction, drug discovery, and computer vision applications involving 3D data.

Course Details

  • Curriculum: Master Computer Science
  • Registration: Basis
  • Material: eCampus
  • Effort: 2SWS lecture + 2SWS exercise, 6CP
  • Exam: 19.02.26 10:00-12:00 and 27.03.26 10:00-12:00

Lecture

  • Time: Thursdays, 10-12 c.t.
  • Place: Friedrich-Hirzebruch Allee 5 - Hörsaal 3
  • First lecture: 16.10.25

Exercise

  • Time: Tuesday, 10-12 c.t.
  • Place: Friedrich-Hirzebruch Allee 6-8 - Room 3.035b
  • First exercise: 28.10.25 (or 04.11.25)